The Impact of Proactive Fecal Calprotectin Collection in an Outreach Protocol for Biologic-Naïve Ulcerative Colitis Patients–Ulcerative Colitis Clinical Outreach (UCCO)
Bibliographic record
Abstract
BACKGROUND: Ulcerative colitis (UC) is a chronic, relapsing inflammatory bowel disease that requires regular monitoring. The University of Alberta IBD Unit piloted a proactive outreach protocol for biologic-naïve UC patients, including clinical and biochemical variables, and assessed its impact on UC care. METHODS: Biologic-naïve UC patients without follow-up for ≥6 months were recruited by phone and completed Partial Mayo, modified Sutherland Index, and MARS-5 questionnaires, as well as blood work and fecal calprotectin (FCP). Results were sent to each patient's gastroenterologist, who then completed a survey about intended UC management changes. RESULTS: 81 patients completed the protocol. UC management was changed in 45 (55.6%) cases, with 82.2% of changes being expedited follow-up or management escalation. Six patients had active flares, and 17 with asymptomatic inflammation were identified. 23 patients underwent endoscopy, with 10 (43.4%) showing active disease. Six patients started biologic therapies based on protocol and endoscopic findings. UC management escalations were significantly predicted by FCP and Sutherland Index scores on logistic regression analysis. 86.4% of gastroenterologists rated the protocol helpful. CONCLUSIONS: Patient care can be improved by a one-time, proactive outreach program for biologic-naïve UC. Outreach and monitoring in biologic-naïve UC should include assessment of both FCP and clinical markers to improve UC management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".